Partitioning hard clustering algorithms based on multiple dissimilarity matrices

DOI10.1016/j.patcog.2011.05.016zbMath1225.68228OpenAlexW2139686646MaRDI QIDQ645877

Filipe M. De Melo, Yves Lechevallier, Francisco de. A. T. de Carvalho

Publication date: 10 November 2011

Published in: Pattern Recognition (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.patcog.2011.05.016

zbMATH Keywords

relational datamultiple dissimilarity matricespartitioning clustering algorithmsrelevance weight

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Pattern recognition, speech recognition (68T10) Data structures (68P05)

Related Items (4)

Active constrained fuzzy clustering: a multiple kernels learning approach ⋮ Nonlinear multicriteria clustering based on multiple dissimilarity matrices ⋮ Combining dissimilarity matrices by using rank correlations ⋮ A fast multiobjective fuzzy clustering with multimeasures combination

Uses Software

• J-MEANS
• UCI-ml
• ROCK

Cites Work

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• Relational duals of the c-means clustering algorithms
• Efficient algorithms for divisive hierarchical clustering with the diameter criterion
• Clustering and aggregation of relational data with applications to image database categorization
• Finding Groups in Data
• A monothetic clustering method
• Dissimilarity measures for time trajectories
• J-MEANS: A new local search heuristic for minimum sum of squares clustering

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